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2005
IEEE

Neighborhood Formation and Anomaly Detection in Bipartite Graphs

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Neighborhood Formation and Anomaly Detection in Bipartite Graphs
Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (Neighborhood formation), and 2) finding abnormal nodes (Anomaly detection). And we propose algorithms to compute the neighborhood for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify abnormal nodes, using neighborhood information. We evaluate the quality of neighborhoods based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.
Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Chri
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICDM
Authors Jimeng Sun, Huiming Qu, Deepayan Chakrabarti, Christos Faloutsos
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